Addressing Food Insecurity Through Community Kitchens During the COVID-19 Pandemic: A Case Study from the Eastern Cape, South Africa
Bibliographic record
Abstract
One of the most critical impacts of the COVID-19 pandemic was on food security. Food insecurity increased in many communities, with some showing signs of resilience through autonomously creating community kitchens that enhanced food security and built support networks. These initiatives filled gaps left by government programmes and provided a critical lifeline for vulnerable communities during the pandemic, fostering community solidarity. This paper aims to investigate the experiences and perceptions of community kitchen managers in addressing food insecurity during the COVID-19 pandemic by using a town in South Africa in 2020–2022 as a case study. Using arts-based participatory approaches, researchers interviewed 11 community kitchen managers representing 10 community kitchens in four sessions between June and November 2021. The results showed that a lack of jobs and food insecurity were identified as the main threats, whereas COVID-19 was not even identified as a threat by all of the community kitchen managers. Lacking support from the local government, these initiatives depended on individuals and community-based organisations for backing. However, this support decreased in 2021 and 2022, raising concerns about the sustainability of these efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".